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<a href="_subrange_kernel_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Variant of WeightedSumKernel which works on subranges of Vector inputs</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \author      S., O.Krause</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \date        2012</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * </span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * </span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * </span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#ifndef SHARK_MODELS_KERNELS_SUBRANGE_KERNEL_H</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#define SHARK_MODELS_KERNELS_SUBRANGE_KERNEL_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_weighted_sum_kernel_8h.html">shark/Models/Kernels/WeightedSumKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="keyword">namespace </span>detail{<span class="comment"></span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">/// \brief given two vectors of input x = (x_1,...,x_n), y = (y_1,...,y_n), a subrange 1&lt;=k&lt;l&lt;=n and a kernel k, computes the result of</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">///   the subrange k((x_k,...x_l),(y_k,...,y_l))</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">/// \ingroup kernels</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType&gt;</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="keyword">class </span>SubrangeKernelWrapper : <span class="keyword">public</span> AbstractKernelFunction&lt;InputType&gt;{</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>    <span class="keyword">typedef</span> AbstractKernelFunction&lt;InputType&gt; base_type;</div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#adbf700c2ece7236c70cef4b88777a733" title="batch input type of the kernel">base_type::BatchInputType</a> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#adbf700c2ece7236c70cef4b88777a733" title="batch input type of the kernel">BatchInputType</a>;</div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#a40e365cb5ec7d2776105a4aef4e78df3" title="Const references to InputType.">base_type::ConstInputReference</a> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#a40e365cb5ec7d2776105a4aef4e78df3" title="Const references to InputType.">ConstInputReference</a>;</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#af923f26f3d015156bb5ac159b302311b" title="Const references to BatchInputType.">base_type::ConstBatchInputReference</a> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#af923f26f3d015156bb5ac159b302311b" title="Const references to BatchInputType.">ConstBatchInputReference</a>;</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span> </div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>    SubrangeKernelWrapper(AbstractKernelFunction&lt;InputType&gt;* kernel,std::size_t start, std::size_t end)</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    :m_kernel(kernel),m_start(start),m_end(end){</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>        <span class="keywordflow">if</span>(kernel-&gt;hasFirstParameterDerivative())</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>            this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_kernel_function.html#aa13e9ab3b8bbad9e1d773468671703e6">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_kernel_function.html#af54c80ca837961761506e6c2eec15bdead621a9ae065d91a154055a38a7ea72f8" title="is the kernel differentiable w.r.t. its parameters?">base_type::HAS_FIRST_PARAMETER_DERIVATIVE</a>;</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>        <span class="keywordflow">if</span>(kernel-&gt;hasFirstInputDerivative())</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>            this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_kernel_function.html#aa13e9ab3b8bbad9e1d773468671703e6">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_kernel_function.html#af54c80ca837961761506e6c2eec15bdeae4bd575af084f862f64bc665cad4c4ec" title="is the kernel differentiable w.r.t. its inputs?">base_type::HAS_FIRST_INPUT_DERIVATIVE</a>;</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>    }</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment"></span> </div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_i_nameable.html#a9893f99314de30cd472e649c235d0db4" title="returns the name of the object">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;SubrangeKernelWrapper&quot;</span>; }</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span> </div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>    RealVector <a class="code hl_function" href="classshark_1_1_i_parameterizable.html#afaa2ba692ab64a0edbff60d7ee6794db" title="Return the parameter vector.">parameterVector</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>        <span class="keywordflow">return</span> m_kernel-&gt;parameterVector();</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>    }</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span> </div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_i_parameterizable.html#ad5e35d1a10ff36fa72ea787baa40e9ad" title="Set the parameter vector.">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters) {</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        m_kernel-&gt;setParameterVector(newParameters);</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>    }</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span> </div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>    std::size_t <a class="code hl_function" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20" title="Return the number of parameters.">numberOfParameters</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        <span class="keywordflow">return</span> m_kernel-&gt;numberOfParameters();</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    }</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment"></span> </div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">    ///\brief creates the internal state of the kernel</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment"></span>    boost::shared_ptr&lt;State&gt; <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a9057a4a71b4d28febb171e09bbd22c07" title="Creates an internal state of the kernel.">createState</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        <span class="keywordflow">return</span> m_kernel-&gt;createState();</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>    }</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span> </div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(ConstInputReference x1, ConstInputReference x2)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <span class="keywordflow">return</span> m_kernel-&gt;eval(blas::subrange(x1,m_start,m_end),blas::subrange(x2,m_start,m_end));</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>    }</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span> </div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix&amp; result, State&amp; state)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>        m_kernel-&gt;eval(columns(batchX1,m_start,m_end),columns(batchX2,m_start,m_end),result,state);</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>    }</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span> </div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix&amp; result)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>        m_kernel-&gt;eval(columns(batchX1,m_start,m_end),columns(batchX2,m_start,m_end),result);</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    }</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span> </div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a48557b9834bc06ccb4e005ce441904c8" title="Computes the gradient of the parameters as a weighted sum over the gradient of all elements of the ba...">weightedParameterDerivative</a>(</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        ConstBatchInputReference batchX1,</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        ConstBatchInputReference batchX2,</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>        RealMatrix <span class="keyword">const</span>&amp; coefficients,</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        State <span class="keyword">const</span>&amp; state,</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        RealVector&amp; gradient</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>    )<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        m_kernel-&gt;weightedParameterDerivative(</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>            columns(batchX1,m_start,m_end),</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>            columns(batchX2,m_start,m_end),</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>            coefficients,</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>            state,</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>            gradient</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>    );</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>    }</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#af534a7a45f73baab879c2f0bfb75f00a" title="Calculates the derivative of the inputs X1 (only x1!).">weightedInputDerivative</a>(</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        ConstBatchInputReference batchX1,</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        ConstBatchInputReference batchX2,</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        RealMatrix <span class="keyword">const</span>&amp; coefficientsX2,</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        State <span class="keyword">const</span>&amp; state,</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        BatchInputType&amp; gradient</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    )<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        BatchInputType temp(gradient.size1(),m_end-m_start);</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        m_kernel-&gt;weightedInputDerivative(</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>            columns(batchX1,m_start,m_end),</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>            columns(batchX2,m_start,m_end),</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>            coefficientsX2,</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>            state,</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>            temp</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        );</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        ensure_size(gradient,batchX1.size1(),batchX2.size2());</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        gradient.clear();</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        noalias(columns(gradient,m_start,m_end)) = temp;</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>    }</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span> </div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>    <span class="comment">//w don&#39;t need serializing here, this is done by the implementing Kernel</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_abstract_metric.html#a8286ec6f54f35ab53a92d42cb251d6e4" title="From ISerializable, reads a metric from an archive.">read</a>(<a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a>&amp; ar){</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>    }</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_abstract_metric.html#a525c9c1f3d9af398bb257b8e42cafe24" title="From ISerializable, writes a metric to an archive.">write</a>(<a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a>&amp; ar)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>    }</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span> </div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>    AbstractKernelFunction&lt;InputType&gt;* m_kernel;</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>    std::size_t m_start;</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>    std::size_t m_end;</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>};</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span> </div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType&gt;</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span><span class="keyword">class </span>SubrangeKernelBase</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>{</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span> </div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Kernels,<span class="keyword">class</span> Ranges&gt;</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>    SubrangeKernelBase(Kernels <span class="keyword">const</span>&amp; kernels, Ranges <span class="keyword">const</span>&amp; ranges){</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(kernels.size() == ranges.size());</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != kernels.size(); ++i){</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>            m_kernelWrappers.push_back(</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>                SubrangeKernelWrapper&lt;InputType&gt;(kernels[i],ranges[i].first,ranges[i].second)</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>            );</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        }</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>    }</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span> </div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>    std::vector&lt;AbstractKernelFunction&lt;InputType&gt;* &gt; makeKernelVector(){</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        std::vector&lt;AbstractKernelFunction&lt;InputType&gt;* &gt; kernels(m_kernelWrappers.size());</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != m_kernelWrappers.size(); ++i)</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>            kernels[i] = &amp; m_kernelWrappers[i];</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>        <span class="keywordflow">return</span> kernels;</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>    }</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span> </div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>    std::vector&lt;SubrangeKernelWrapper &lt;InputType&gt; &gt; m_kernelWrappers;</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>};</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>}</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span><span class="comment"></span> </div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span><span class="comment">/// \brief Weighted sum of kernel functions</span></div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span><span class="comment">///</span></div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span><span class="comment">/// For a set of positive definite kernels \f$ k_1, \dots, k_n \f$</span></div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span><span class="comment">/// with positive coeffitients \f$ w_1, \dots, w_n \f$ the sum</span></div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span><span class="comment">/// \f[ \tilde k(x_1, x_2) := \sum_{i=1}^{n} w_i \cdot k_i(x_1, x_2) \f]</span></div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span><span class="comment">/// is again a positive definite kernel function. This still holds when</span></div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span><span class="comment">/// the sub-kernels only operate of a subset of features, that is, when</span></div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span><span class="comment">/// we have a direct sum kernel ( see e.g. the UCSC Technical Report UCSC-CRL-99-10:</span></div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="comment">/// Convolution Kernels on Discrete Structures by David Haussler ).</span></div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment">///</span></div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span><span class="comment">/// This class is very similar to the #WeightedSumKernel , except that it assumes it&#39;s</span></div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span><span class="comment">/// inputs to be tuples of values \f$ x=(x_1,\dots, x_n) \f$ and we calculate the direct</span></div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span><span class="comment">/// sum of kernels</span></div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span><span class="comment">/// \f[ \tilde k(x, y) := \sum_{i=1}^{n} w_i \cdot k_i(x_i, y_i) \f]</span></div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span><span class="comment">///</span></div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span><span class="comment">/// Internally, the weights are represented as \f$ w_i = \exp(\xi_i) \f$</span></div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span><span class="comment">/// to allow for unconstrained optimization.</span></div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span><span class="comment">///</span></div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span><span class="comment">/// The result of the kernel evaluation is devided by the sum of the</span></div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span><span class="comment">/// kernel weights, so that in total, this amounts to fixing the sum</span></div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span><span class="comment">/// of the weights to one.</span></div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType, <span class="keyword">class</span> InnerKernel=WeightedSumKernel&lt;InputType&gt; &gt;</div>
<div class="foldopen" id="foldopen00190" data-start="{" data-end="};">
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"><a class="line" href="classshark_1_1_subrange_kernel.html">  190</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_subrange_kernel.html" title="Weighted sum of kernel functions.">SubrangeKernel</a></div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>: <span class="keyword">private</span> detail::SubrangeKernelBase&lt;InputType&gt;<span class="comment">//order is important!</span></div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>, <span class="keyword">public</span> InnerKernel</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>{</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>    <span class="keyword">typedef</span> detail::SubrangeKernelBase&lt;InputType&gt; base_type1;</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span><span class="comment"></span> </div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00199" data-start="{" data-end="}">
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"><a class="line" href="classshark_1_1_subrange_kernel.html#ad3d16c38f2af5e5cb43bcf716bea225b">  199</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_subrange_kernel.html#ad3d16c38f2af5e5cb43bcf716bea225b" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;SubrangeKernel&quot;</span>; }</div>
</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span> </div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Kernels,<span class="keyword">class</span> Ranges&gt;</div>
<div class="foldopen" id="foldopen00203" data-start="{" data-end="}">
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"><a class="line" href="classshark_1_1_subrange_kernel.html#af74f43289f60c804b43e0c87305b8991">  203</a></span>    <a class="code hl_function" href="classshark_1_1_subrange_kernel.html#af74f43289f60c804b43e0c87305b8991">SubrangeKernel</a>(Kernels <span class="keyword">const</span>&amp; kernels, Ranges <span class="keyword">const</span>&amp; ranges)</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>    : base_type1(kernels,ranges)</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>    , InnerKernel(base_type1::makeKernelVector()){}</div>
</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>};</div>
</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span> </div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"><a class="line" href="namespaceshark.html#ab20663bec1e12ec9b4bb82f6f1a42307">  208</a></span><span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_subrange_kernel.html" title="Weighted sum of kernel functions.">SubrangeKernel&lt;RealVector&gt;</a> <a class="code hl_typedef" href="namespaceshark.html#ab20663bec1e12ec9b4bb82f6f1a42307">DenseSubrangeKernel</a>;</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"><a class="line" href="namespaceshark.html#aefea6fa9f7fe9c45eeb2715cab40dbd6">  209</a></span><span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_subrange_kernel.html" title="Weighted sum of kernel functions.">SubrangeKernel&lt;CompressedRealVector&gt;</a> <a class="code hl_typedef" href="namespaceshark.html#aefea6fa9f7fe9c45eeb2715cab40dbd6">CompressesSubrangeKernel</a>;</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span> </div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>}</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span><span class="preprocessor">#endif</span></div>
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